Since the first successful insertion of a man-made object into orbit about the Earth, engineers and scientists have continued to develop more complex satellites performing increasingly complex missions. The functionality of space-borne assets has increased dramatically from the simple beacon transmission of the Sputnik satellite. As the capability of launch systems and satellite components increase, it is possible to put larger and more complex hardware into orbit. With each mission and continued advancement of spacecraft technology, missions have ventured farther out from Earth to our planetary neighbors, such as the Messenger mission to Mercury, and distant locales, such as the New Horizons mission to Pluto and Charon. As the missions and spacecraft have become more complex, the requirements on spacecraft navigation become more stringent.
Even with advanced celestial dynamics models and navigation measurements, it is not possible to perfectly predict spacecraft ephemeris (trajectory over time). This is due to the complexity of the system being modeled, and assumptions used in the modeling of dynamic effects in deep space. As such, there is inherent error in predicting and propagating a spacecraft's trajectory as accuracy is limited due to a range of issues from gravitational model uncertainty to finite precision computations. Due to these effects, most spacecraft mission concepts include several trajectory correction maneuvers (TCM) in order to tweak the spacecraft's orbit. These corrections are needed to ensure correct planetary flyby conditions, orbit insertions, and correct for initial orbit insertion errors. The primary information used to plan a TCM is a comparison between the navigation data and the design trajectory. The effectiveness of the TCM is limited by the accuracy of the estimated state and other on-board systems. Orbit observations allow for an analysis of the as-flown trajectory compared to the planned TCM. Ground analysis compares the observed to the predicted state and desired state to ascertain if any thrust is needed during the planned trajectory. Improvements in both navigation accuracy and state update rate will increase knowledge of spacecraft position. This will further reduce the need for large TCMs by the minimization of initial state error and increasing the effectiveness of corrections resulting from increased knowledge of the current state. Overall error is still limited by the assumptions made in the predicted dynamics, which high precision navigation dynamics models can improve.
In addition to trajectory planning concerns, some of the most difficult missions involve landing a probe or rover on an extraterrestrial planetary surface. For scientific missions, it is desired to arrive in a general vicinity of scientific interest or region. But as man begins to push outwards from Earth, the need to deliver supplies to a Lunar or Martian outpost will increase. It will be increasingly important to accurately land these resources close to a predefined location (nearby to the human presence, or within range of any local surface vehicles) to minimize the time and effort required to retrieve the supplies.
Landing systems typically rely on techniques involving aerobraking, braking thrusters, or parachutes to gently land with minimal control during descent. As such, it is paramount to have knowledge of the spacecraft's state well ahead of planetary entry. This knowledge allows ground operators to predict the spacecraft's entry vector and estimated landing site. Accordingly, by increasing accuracy of the navigation knowledge, ground operators can issue more precise thrusting commands to the spacecraft to tune its entry trajectory. Thus, increased navigation performance allows for the capability of precise directed planetary entries and landing.
The navigation problem of ascertaining a vehicle's current position and velocity is a very complex problem. This is due to uncertainties in dynamics models, measurement accuracy, and resolution limitations. For ground navigation, this can be performed relatively accurately with imprecise measurements (such as simply using a map, compass, and observations of the landscape). But as spacecraft travel farther and farther from Earth into deep space, navigation becomes increasingly difficult due to limited observation data and finite precision. As a spacecraft's distance from Earth increases, maintaining a relative positioning accuracy requires continual improvement in observation resolution. At the most fundamental level, numerical precision and computational accuracy limit this capability due to, for example, uncertainty in the dynamic models, physical spacecraft limitations, and the measurement process.
One of the main complexities in the deep space navigation problem is a product of the environment itself and the distances involved. Typically, deep space navigation is performed by Earth-based assets. However, the time for a signal to reach its destination can vary from several minutes to several hours based on the geometry involved. The transmission travel time, along with the time required for post-processing and analysis on the ground, produces a latency in any state measurement based on this observation. As such, a calculated navigation solution is a measurement of where the spacecraft was and not where it currently is. This delay also affects transmission capabilities, in that the ground-based assets must be “pointed” (i.e., pointing direction of an asset's antenna) based on the predicted delay and where the satellite will be when the signal has traveled such a distance. Ground assets must therefore track ahead of the spacecraft. Additionally, the efficiency of transmitting to a spacecraft is driven by knowledge of the spacecraft's position, which can drive pointing losses that can limit data transmission rates. As navigation fixes are generated, errors in pointing and data transmission can be reduced.
Due to the issues involved with signal travel and deep space communication, the ideal deep space navigation solution involves performing navigation autonomously on-board a spacecraft. There are several obstacles to using on-board satellite systems to perform complex navigation and state estimation routines. Two considerable obstacles are algorithm development and the required hardware and computational systems. Due to the long planning time associated with deep space missions and the desire to use flight-proven systems, the amount of computing power limits the implementation of advanced algorithms. In addition, hardware availability is an issue due to requirements on radiation hardening that also limits on-board memory storage. Due to these limitations, it is difficult to develop autonomous algorithms of sufficient capability to be run on memory-constrained and processing-constrained systems.
Additionally, spacecraft are physically constrained due to launch vehicle limitations related to vehicle volume and mass. This constrains which and how many instruments can be installed on the spacecraft. For example, to transmit information back to Earth, a large directional antenna is required. Still further, powering sophisticated hardware becomes a critical issue as a spacecraft travels farther from the sun. That is, a spacecraft's electrical power is generally derived using solar energy. However, solar flux reduces proportionally to the square of the distance. Thus, solar panels are decreasingly useful the farther a spacecraft travels from the sun. The availability of on-board power and its distribution across multiple subsystems limits the use of high-power computing resources.
To summarize, any on-board navigation hardware needs to have limited power requirements and a minimal effect on other spacecraft operations. Due to these factors, deep space navigation is inherently difficult due to the environment involved, the signal delay, and the tight spacecraft physical and operational constraints. Many methods of navigation currently used are external to the spacecraft. However, as autonomy and navigational accuracy requirements increase, it is necessary to shift navigation-related functions to an on-board scheme. This is particularly important in scenarios where the required response time for the spacecraft is faster than the time required for communication to Earth with ground-based external analysis. For this to happen, a spacecraft must be able to accurately update its own state estimate in order to accomplish it prescribed mission.